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Published in:
2021 | OriginalPaper | Chapter
Machine Learning Approach for Contactless Photoplethysmographic Measurement Verification
Authors : Ivan Semchuk, Natalia Muravskaya, Konstantin Zlobin, Andrey Samorodov
Published in: Pattern Recognition. ICPR International Workshops and Challenges
Publisher: Springer International Publishing
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Abstract
Contactless heart rate measurement techniques can be applied in medical and biometrical tasks such as vital signs measurement and vitality detection. Incorrect measurement result can cause serious consequences. In this paper a method for contactless heart rate measurement result verification is proposed. A binary classifier is used in order to identify whether a contactless photoplethysmogram (PPG) signal is reliable. Experimental setup used for signal dataset acquisition consists of contact plethysmograph, web-camera and contactless plethysmography device. Feature vector containing various signal and signal’s spectral density metrics as classification algorithms input is used. The highest classification accuracy is shown by classifier based on logistic regression (99.94%). The classification results demonstrate that the proposed method can be used in further contactless methods research.